Mobile users will have more connectivity options as the availability of multiple networking technologies increases. Each connectivity option may affect service quality in unique ways depending on a multitude of factors. At present, users have few ways to accurately and quickly experience expected service quality on a given network before they make their network selection. Users therefore must either rely on operator promises, available metrics of performance pre-selection, such as signal strength, or employ a trial-and-error approach. The latter is time-wasting while the former often confuses naïve users with network engineering terms that they do not fully understand.
Mobile users have more and more options for services. They already manually choose between information services that offer similar value, e.g. mobile web-sites, mobile applications. Today's 4G-5G landscape means that in many areas, especially highly populated ones, there will be a heterogeneous mix of access networks, e.g. WiFi, CDMA, GSM, WiMAX, etc., visible to a mobile device at any time. User devices are capable of, and involved in, complex signaling and connectivity (transparently to user) messaging, e.g., reading Base Station signal strength, handing over between Base Stations (see IP Multimedia Subsystem (IMS), 3GPP, standards, etc.)
In a contract-less world where networks may be used on-the-fly and for short times, choice and mobile user experience become more important to the user as a differentiator. The ability to attach to radio access networks (RANs) in contract-less fashion is becoming real, e.g., contract-less Mobile Virtual Network Operators (MVNOs) such as Zer01.
When multiple links (so-called Layer 2, or L2) and networks (so-called Layer 3 or L3) connectivity services, such as those that supply the mobile device with an IP address and access to the Internet or some other data network, are available to mobile users, a decision must be made to choose one over another. Policy functions are effective in cases where quantitative metrics are the basis for decision and these metrics are available directly. Policy Decision Point (PDP) and/or Policy Enforcement Point (PEP) components effectively implement policy functions which result in a decision being made about something (e.g., an available network) based on some available criterion (e.g., cost of network access). However, in cases where softer experiential or qualitative metrics are important, it is harder to anticipate and convey the impact of a network selection to a user whose mindset is not machine-readable. With the standard PDP choice, the system cannot take into account the quality of experience of using the system, or the nearly impossible-to-quantify emotional aspects of the user which should drive the decision, such as the user's mood, his level of trust of particular options, the urgency of his requirements, the user's future schedule of network needs, and so on. The user herself, however, has an intuitive understanding of all of the these.
While in today's current environment users can choose some aspects of their experience, they cannot choose others (sometimes for good reason). Most of the time, mobile devices make automatic choices (e.g., that strive to optimize performance) without the user in the loop. This works well for decisions made on the basis of complex network engineering aspects that users do not understand, such as bit rates, loss, jitter, and so on. On the other hand, Quality of Experience (QoE), the subjective measure of performance in a system, is of rising importance in all user services industries, including Web and mobile. In general, users may want a say in how options (usually selected beyond their control) are selected, e.g., networks used, etc., in order to optimize their subsequent experiences with the services. Operators and service providers may prefer that some choices (and ramifications) are left to the customer rather than their computing platform or PDP/PEP (e.g., they may have liability). Pushing choice up to the user is empowering to the user, who understands QoE in an intuitive way (“I know good QoE when I see it!”). Yet no tools exist that employ interactive QoE evaluation at the moment when L2/L3 attachment is possible.
Because multi-networks and multi-mode devices are relatively new, multimedia computing capabilities are only recently at a sufficiently high level, and contract-less network attachments are relatively new, there has been little work specifically addressing user selection of point of attachment guided by interactive QoE measurements and QoE-based suggestions. New demands of mobile users, device capabilities, and network proliferation are only presently generating the need for these capabilities.
Existing systems have successfully used policy to analyze quantitative metrics of networks in order to choose the most appropriate one at a given moment. These metrics may include those related to quality of service (QoS), e.g. jitter, delay, etc., or any other quantifiable metrics. The industry groups 3GPP (Third Generation Partnership Program) and OMA (Open Mobile Alliance) have documents describing this. Existing systems, however, have not addressed how the QoE across several networks can be interactively presented to a mobile user in the form of emulations, or other visual means, nor how the user's use of Web browsing applications can be supported by real-time network and link level intelligence to increase user experience.
In the current art, Quality of Service (QoS) can be conveyed via low level metrics such as signal strength or bit rate. For QoE, however, these are very coarse indicators and end users do not understand the relationship between these indicators and QoE. Mobile operators already provide QoS-based Service Level Agreements (SLAs) between themselves and MVNOs and others paying to lease or use their networks, and so a variety of techniques exist to measure and test QoS. Network monitoring and surveillance products can monitor links in a communications network and can emit alerts when link performance might threaten prescribed QoS. There are many such QoS-related products. In practice, some techniques also exist to measure and test QoE for mobile devices. Such techniques can include end to end monitoring, probes, and/or on-device software and human testers. Principally however, the user is involved in QoE measurement as QoE is—at least in part—a subjective measure of quality made by a given user.
Further, today's mobile devices almost all have Web browsers and have the capability to access information from the Internet. Today's browsers include Internet Explorer®, Opera®, Firefox®, Safari®, and others; different versions of these exist for different platforms (e.g., for mobile devices with a particular OS). These Web browsers implement the Hypertext Transfer Protocol (HTTP) protocol and support (to some extent) the HTML standards as defined by W3C. Notably, an HTML page can be viewed by a Web browser and contains embedded links, multimedia and data. The links on a give page are activated when a user selects them (e.g., with a mouse pointer or gestural motion), and cause the browser to load the resources pointed to by the link. On today's sometimes loaded mobile networks, it can be frustrating for users browsing the Web on these devices to access information. A common use case is a mobile user clicking on a link that leads to data-intensive content (e.g., video streaming) even though the network he is currently attached to is not truly optimal to support such content. Another current problem is that today's systems do not anticipate the linked content that a user will choose, nor do they correlate the properties of that linked content to network capabilities to offer suggestions for improved QoE on given linked content.
Further, the notion of “ranking” Web pages for importance or authority is well studied. It is embodied, for example, in Google's PageRank®. Ranking algorithms may be highly proprietary or use best-practices or known algorithms. A fundamental algorithm for ranking a Web page's so-called importance is to count the number of other pages that make reference to it in links and use that as a rank. Measuring and estimating Web performance is also part of the current art. This includes techniques for estimating a Web page load time based on its size, composition, e.g. web sites offering optimization, and so on.
Web Content preview (window-in-window) via, for example, the CoolPreviews® (by CoolIris inc.) tool allows a Web browser user to “preview” the contents of a page's link by hovering over it with the pointing device. Google® also uses this approach on search results pages. This can be useful for users to decide if the linked-to page merits browsing. Upon hover the Web browser loads the link contents (or a portion) and renders it in a small window for preview. Users find this useful because the parent window remains visible and the previewed window is layered over top of the parent window and can be moved or closed easily yet it previews the link the user would arrive at if selected.
The field of content adaptation aims to adapt Web content so that it is suitable for the mobile device it is viewed upon. This field focuses mostly on the salient issues of the mobile device screen size and page navigation modes and adapting Web pages and Web page elements (e.g., images, audio) to that device. High level approaches to content adaptation include making a device specific version available, adapting content on the fly between the server and client, and using learning approaches and feedback to better understand what adaptation is best for the user.
Hierarchical coding, e.g. of video, is a technique in which media are coded in a manner that allows different qualities to be played out at the discretion of the decoder. This allows, for example, a low resolution version of video to play in resource constrained situations, and a high-resolution one in contexts where network resources are highly available.
Prior solutions to content adaptation have not attempted to take into account the dynamic nature of the surrounding radio access networks (RANs): both the presence of and capabilities of RANs are unpredictable, and content cannot be tailored a priori for it. Optimization techniques are useful in isolation but do not provide an analysis->user-interaction->link-utilization process that is necessary to give mobile users the option for improved quality of experience as a function of anticipated Web content. Prior solutions have not tied mobile Web browsing actions to link characteristics and user experience via dialogs. Existing content adaptation techniques do not address the Link and Radio attachment points.
There is a need for experiential techniques that improve network selection and give mobile users a richer, more relevant and impactful estimate of expected service quality.